class_docstr: Parallel coordinates displays each feature as a vertical axis spaced
  evenly along the horizontal, and each instance as a line drawn between
  each individual axis. This allows you to detect braids of similar instances
  and separability that suggests a good classification problem.
  
  Parameters
  ----------
  ax : matplotlib Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  features : list, default: None
      a list of feature names to use
      If a DataFrame is passed to fit and features is None, feature
      names are selected as the columns of the DataFrame.
  
  classes : list, default: None
      a list of class names for the legend
      The class labels for each class in y, ordered by sorted class index. These
      names act as a label encoder for the legend, identifying integer classes
      or renaming string labels. If omitted, the class labels will be taken from
      the unique values in y.
  
      Note that the length of this list must match the number of unique values in
      y, otherwise an exception is raised.
  
  normalize : string or None, default: None
      specifies which normalization method to use, if any
      Current supported options are 'minmax', 'maxabs', 'standard', 'l1',
      and 'l2'.
  
  sample : float or int, default: 1.0
      specifies how many examples to display from the data
      If int, specifies the maximum number of samples to display.
      If float, specifies a fraction between 0 and 1 to display.
  
  random_state : int, RandomState instance or None
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by np.random; only used if shuffle is True and sample < 1.0
  
  shuffle : boolean, default: True
      specifies whether sample is drawn randomly
  
  colors : list or tuple, default: None
      A single color to plot all instances as or a list of colors to color each
      instance according to its class. If not enough colors per class are
      specified then the colors are treated as a cycle.
  
  colormap : string or cmap, default: None
      The colormap used to create the individual colors. If classes are
      specified the colormap is used to evenly space colors across each class.
  
  alpha : float, default: None
      Specify a transparency where 1 is completely opaque and 0 is completely
      transparent. This property makes densely clustered lines more visible.
      If None, the alpha is set to 0.5 in "fast" mode and 0.25 otherwise.
  
  fast : bool, default: False
      Fast mode improves the performance of the drawing time of parallel
      coordinates but produces an image that does not show the overlap of
      instances in the same class. Fast mode should be used when drawing all
      instances is too burdensome and sampling is not an option.
  
  vlines : boolean, default: True
      flag to determine vertical line display
  
  vlines_kwds : dict, default: None
      options to style or display the vertical lines, default: None
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  n_samples_ : int
      number of samples included in the visualization object
  
  features_ : ndarray, shape (n_features,)
      The names of the features discovered or used in the visualizer that
      can be used as an index to access or modify data in X. If a user passes
      feature names in, those features are used. Otherwise the columns of a
      DataFrame are used or just simply the indices of the data array.
  
  classes_ : ndarray, shape (n_classes,)
      The class labels that define the discrete values in the target. Only
      available if the target type is discrete. This is guaranteed to be
      strings even if the classes are a different type.
  
  Examples
  --------
  
  >>> visualizer = ParallelCoordinates()
  >>> visualizer.fit(X, y)
  >>> visualizer.transform(X)
  >>> visualizer.show()
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docstr: Called from the fit method, this method creates the parallel
  coordinates canvas and draws each instance and vertical lines on it.
  
  Parameters
  ----------
  X : ndarray of shape n x m
      A matrix of n instances with m features
  
  y : ndarray of length n
      An array or series of target or class values
  
  kwargs : dict
      Pass generic arguments to the drawing method
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code: def draw(self, X, y, **kwargs):
      """
      Called from the fit method, this method creates the parallel
      coordinates canvas and draws each instance and vertical lines on it.
  
      Parameters
      ----------
      X : ndarray of shape n x m
          A matrix of n instances with m features
  
      y : ndarray of length n
          An array or series of target or class values
  
      kwargs : dict
          Pass generic arguments to the drawing method
  
      """
      if self.fast:
          return self.draw_classes(X, y, **kwargs)
      return self.draw_instances(X, y, **kwargs)
  
file_path: yellowbrick/yellowbrick/features/pcoords.py
imports: import numpy
  from numpy.random import RandomState
  from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler
  from sklearn.preprocessing import Normalizer, StandardScaler
  from yellowbrick.draw import manual_legend
  from yellowbrick.features.base import DataVisualizer
  from yellowbrick.utils import is_dataframe, is_series
  from yellowbrick.exceptions import YellowbrickTypeError, YellowbrickValueError
label_desc: Use image node_id 3 for calling the ParallelCoordinates obj's underlying member method code with example usage: obj.draw(X, y) and returns: self, self